Optimisation of multiple feature stream weights for distributed speech processing in mobile environments

Abstract

Mobile environments are highly influenced by ambient noise that can cause a significant deterioration in speech recognition performance. In this study, a new framework integrating a noise-robust frontend (FE) in distributed speech recognition (DSR) is presented. Using the Aurora-2 speech database, the authors evaluate the impact of the proposed multidimensional acoustical analysis on the performance of the Mel-frequency-based European Telecommunications Standards Institute-advanced FE (AFE) combined with the Mel-line spectral frequencies (MLSFs) robust features for highly noisy speech. The stream weights of the resulting multi-stream hidden Markov models are optimised automatically by deploying a novel approach based on a discriminative model combination. Finally, these features are effectively transformed and reduced using the Karhunen-Loève transform. The proposed MLSF-based FE (MLSF-FE) is shown to exhibit a reduction in the relative error rate. Moreover, the proposed FE provides comparable recognition performance to the current DSR-AFE available in global system of mobile communications.